Identification of microbes (such as bacteria, archaea and simple eucarya) using DNA and other hybridization probes has become increasingly sophisticated and accurate as probe technology and methods have improved. Probes are molecules that bind with high affinity and specificity to target molecules. Up to now, instrumentation for detecting fluorescently-labeled or other spectroscopically identifiable probes has not been able to fully exploit the capabilities of such molecules to identify in situ highly complex (i.e., genotypically or phenotypically diverse) mixtures of biological cells and viruses. Positive identification of single variant organisms in large populations of similar cells is also problematic. Fluorescent and other spectroscopically identifiable labeling methods are ultimately limited by the number of different spectral xe2x80x98fingerprintsxe2x80x99 that can be distinguished by the imaging system that is used to measure and sort them. We have created probe sets labeled with as many as eight distinct fluorophores for simultaneous hybridization against 16S small subunit RNA and other targets. These fluorophores are all organic dyes with relatively broad absorption and fluorescence emission bands; however, newly developed inorganic fluorescent quantum dots or nanocrystals (with narrower fluorescence emission bands) can also be used. KAIROS has developed instrumentation and spectral deconvolution and sorting algorithms to increase the number of spectroscopically identifiable tags that can be simultaneously distinguished, thus enabling accurate xe2x80x98fingerprintingxe2x80x99 of bacteria, archaea and eucarya. We have applied these libraries of probes and the spectral deconvolution software to correctly identify highly complex mixtures of cells in situ by spectral sorting. This new technology for multispectral taxonomic identification (MTID) will benefit clinical and environmental microbiology as well as biotechnology. This instrumentation can also be used with other types of multispectral probes, such as fluorescently labeled antibodies.
Identification of microorganisms, eucaryotic cells, and viruses by a variety of methods has become an essential diagnostic tool in areas such as healthcare, food and water quality testing, and enzyme discovery (Amann et al., 1992; O""Hara et al., 1993; Vandamme, E. J., 1994; Birnbaum et al., 1994; Vandamme, P., 1996; Relman, 1998; Schrenk et al., 1998). Identification is an integral part of biological taxonomy, or the classification of organisms. Its medical uses include confirming bacterial serotypes for epidemiological studies (Birnbaum et al., 1994) and monitoring of nosocomial infection (Andersen, 1995). Environmental uses include analysis of water, soil and air, as well as bioremediation monitoring (Schrenk et al., 1998) and studies of population ecology and bacterial phylogenetics (Pace et al., 1986; Ward et al., 1992; Amann et al., 1995). In biotechnology, taxonomic identification can be used for biodiversity screening, bioprocess monitoring and genomic analysis (Amann et al., 1992; Hoheisel, 1997; Head et al., 1998). Traditionally, microbiologists performing bacterial identification have relied on cultivation of organisms, despite the realization that most of them ( greater than 99%) are not cultivable by standard methods (Amann et al., 1995; Pace, 1997; Head et al., 1998; Hugenholtz et al., 1998b). Many of these culture-based methods rely on chemical analysis of phenotypic characteristics. For example, there are numerous phenotype-based systems for identifying bacterial and archaeal cultures according to their cellular fatty acid ester content (Osterhout et al., 1991), endogenous enzyme activity and/or antibiotic resistance patterns (O""Hara et al., 1993), and antigenic markers (Porter et al., 1993). In the case of antigenic markers, fluorescently labeled antibodies can be used to specifically identify bacterial serotypes, such as the common food pathogen, E. coli 0157:H7 (Restaino et al., 1997; Seo and Frank, 1999). This technique is useful for accurately identifying microorganisms at the species and subspecies level. Recent advances in combinatorial mutagenesis and phage-display technology have also made it possible to create peptides and proteins that have the affinity and specificity of antibodies but are not derived from antibody molecules per se.
More recently, molecular based methods have been developed to examine he diversity of microorganisms without the need to isolate or culture them. One class of methodology takes advantage of the conserved nature of protein synthesis in all cellular organisms. With about 10,000 partial or complete sequences now available for comparison, the small subunit ribosomal RNA (rRNA) (which contains the 16S rRNA in bacteria and archaea and the 18S rRNA in eucarya) is currently the molecule of choice for identifying organisms at the species level. Other rRNA targets include the large subunit 5S or 23S rRNA (in bacteria and archaea) and the large subunit 5S, 5.8S and 28S rRNA (in eucarya). Molecular strategies based on PCR, cloning, sequencing, and probing have enabled biologists to examine the total microbial community in a sample without any a priori knowledge of the species present in the mixture (Amann et al., 1995). Although rRNA-based identification is only accurate to approximately the level of species, its tremendous versatility makes it extremely valuable for high-throughput screening and identification of microorganisms.
The information gained from 16S/18S rRNA sequence comparisons can be used to deduce detailed phylogenetic relationships based on evolution. An evolutionary distance map generated from 16S rRNA sequence data highlights the major lineages of Bacteria and Archaea (FIG. 1). The highly conserved portions of 16S/18S rRNA are ideal for designing primers that will amplify 16S/18S rRNA genes from all three domains of life (Bacteria, Archaea, and Eucarya). At the other extreme, primers can be designed to highly variable regions of 16S/18S rRNA and thus amplify only a particular species or genus in a mixture of microorganisms. Likewise, fluorescent DNA hybridization probes based on 16S/18S sequencing information can be constructed to identify organisms in a large group (i.e., phylum) or in a localized group (i.e., genus), depending on whether the probe sequence is complementary to a conserved or variable region of the 16S/18S rRNA, respectively. Ribosomal RNA is a particularly convenient and attractive hybridization target for quantitative microscopy because a typical E. coli cell contains approximately 20,000 ribosomes (Neidhardt, 1987), and thus xcx9c20,000 copies of the target sequence. These probes can also be made using polymers other than DNA. Such polymers include RNA as well as nucleic acid analogues, such as peptide-nucleic acids, phosphorothioates, and morpholinos. Probes can be covalently labeled with fluorophores or other spectroscopically identifiable labels to enable in situ hybridization and identification by fluorescence or other spectroscopic imaging microscopy (Amann et al., 1990). The probes can also contain fluorophores designed to be FRET (fluorescence resonance energy transfer) pairs, such as molecular beacons.
PCR has been an extremely powerful tool for analyzing samples and constructing databases of sequences. It has been used to amplify the 16S-rDNA genes from microorganisms isolated from highly diverse and extreme environments, as well as from clinical sources (Hugenholtz et al., 1998b; Relman, 1998). Unknown organisms are being identified at the level of new phyla, expanding on the bacterial line of descent. Many of these new phyla do not have cultured representatives, and yet PCR analysis indicates that they are abundant in the environment. These organisms are completely novel, and they may be a rich source of new antibiotics, enzymes, and other bioactive compounds for medicine and biotechnology (Short, 1997). Recently, attempts have been made to reduce the sequencing load and to increase the screening throughput by employing restriction fragment length polymorphism (RFLP) analysis to examine the diversity of these microbial populations. To design actual probes however, a full length 16S rRNA sequence is needed, and it must be aligned into an existing database. The methodologies for bacterial identification by molecular techniques are outlined in FIG. 2.
As we noted above, the etiology of human infections has historically relied on cultivation to identify the responsible microorganisms. Isolation and inoculation of cultured microbes is the conventional means to link causation of disease to a particular pathogen. However, microbes that are difficult or impossible to culture with present techniques can cause some human clinical syndromes that were originally thought to be nonmicrobial. Indeed, a number of pathological conditions are known to be the result of uncultivated bacteria (Fredricks and Relman, 1996; Lorber, 1996). A few examples using molecular methods to identify uncultured bacteria have been reported. The causative agent of Whipple""s disease, for instance, is resistant to culture, but could be identified using PCR and 16S ribosomal RNA sequence analysis (Relman et al., 1992). Additionally, the PCR approach has been used to identify the Whipple bacillus (Tropheryma whippelii) in the eye and mononuclear cells of blood (Rickman et al., 1995; Mxc3xcller et al., 1993). Similarly, the etiologic agents of cat scratch disease and bacillary angiomatosis were identified using PCR-based technology and 16S rRNA analysis (Adal et al., 1994). Sequence analysis identified the agent as a member of the genus Rochalimaea (Proteobacteria; alpha subdivision).
The difficulty of cultivation has also led to the realization that many human infections are more complex than originally thought. The common expectation of syntrophy, where one organism is dependent on the metabolism of anotherxe2x80x94frequently observed in the environmentxe2x80x94may be prevalent in the diseased state as well. Biofilms are a good example of microbial communities, and they have caught the attention of biomedical science, since microbial communities existing as biofilms play a role in both human health and disease. Bacteria that form these biofilms are well known in tooth decay and artificial implants, and are now implicated in other diseases, including kidney, urinary tract and ear infections. Individual species of bacteria in a community may be dependent on other species for survival, which makes isolation in culture a formidable task. Likewise, analyzing such communities in situ using the currently available methods is a difficult undertaking.
Evidence for a complex bacterial population in a disease condition was recently described for prostatitis, a common disease in adult men of all ages (Tanner et al., 1999). Frequently, patients are diagnosed with xe2x80x9cnonbacterialxe2x80x9d prostatitis, but some of these patients respond to antibiotic treatment and show evidence of distinct bacterial species by molecular techniques, despite the absence of cultivable bacteria (Tanner et al., 1999). These species were identified by phylogenetic analyses of their 16S rRNA gene sequences from mixed populations. Prostatitis is an appropriate model to study bacterial identification by mTID imaging because various bacterial species are present, including some that are uncultured, and samples are easy to acquire and process. A second disease amenable to study by MTID is bacterial vaginosis, a prevalent disorder that is probably the result of an imbalance in the various bacterial populations that comprise the vaginal flora. Interestingly, biofilms may play a significant role in the pathology of prostatitis and vaginosis, contributing to the lack of cultured microorganisms in many patients (Potera, 1999).
In addition to PCR/RFLP analysis, other molecular techniques, such as random amplified polymorphic DNA-PCR (RAPD-PCR; Williams et al., 1990) or arbitrarily primed-PCR (AP-PCR; Welsh and McClelland, 1990), DNA or RNA sequencing (Rappxc3xa9 et al., 1998), denaturing gradient gel electrophoresis (DGGE; Muyzer and Smalla, 1998), and micro-array analysis (Dubiley et al., 1997; de Saizieu et al., 1998) can be used to identify microorganisms. However, most of these are not usable by themselves for in situ analysis.
For in situ analysis of these bacterial species, the information gained from PCR and sequencing can be advantageously exploited to synthesize fluorescently labeled or other spectroscopically identifiable oligonucleotide probes for direct hybridization against the 16S rRNA. Up to now, however, these in situ hybridization experiments have utilized no more than one or two probes per sample because of the lack of adequate instrumentation to handle sets of probes that are labeled with fluorophores emitting at several different wavelengths. Moreover, software is not available to design different probes that all have nearly the same melting temperature. The ability to employ multiple probes simultaneously and to analyze them with a calibrated system increases the amount of information that can be obtained from a given sample and substantially increases the overall throughput. In addition, the analysis can be performed on single cells, without extracting, purifying, and amplifying the nucleic acids each time. A whole-cell in situ method such as MTID has numerous advantages over other bacterial identification techniques, including:
Unculturable organisms can be detected
Cells need not be viable
DNA/RNA amplification is not necessary after the sequence is known
Processing is rapid
High throughput is achievable
Unknowns can be tentatively identified via phylogeny
Single cells can be individually analyzed
Rare cells can be detected in complex backgrounds
Once the PCR-based analyses have enabled species-specific hybridization probes for known populations of bacteria (FIG. 2), one can take full advantage of the speed and simplicity of a spectroscopic-based method (i.e., MTID) to streamline the process. Moreover, even if sequence-information is lacking, MTID still enables a level of identification that becomes increasingly accurate as more spectroscopic channels are used to quantitate the hybridization levels of the targeted probes. Unlike identification that is based solely on PCR, MTID is a highly parallel, imaging-based technique that enables in situ analyses and identification of many individual cells within a single field of view.
Due to the fact that greater throughput and increased accuracy of identification can be achieved by using multiple probes, we have chosen to apply the techniques first described for multicolor fluorescence in situ hybridization (FISH) (used in chromosome painting; Schrxc3x6ck et al., 1996; Speicher et al., 1996) for taxonomic identification. There are two methods for actually labeling the probes. For example, in the xe2x80x98ratio labelingxe2x80x99 technique (Nederlof et al., 1992), various fluorescent dyes are combined in specific proportions on each probe. In this case, the relative emission intensity of each dye is used to generate a spectral fingerprint to identify the probe. Thus, the maximum probe complexity (C) is given by the expression C=LDxe2x88x921, where L is the intensity level of the fluorescence emission for each fluorophore and D is the number of different dyes. The simplest variant of this, known as the xe2x80x98combinatorialxe2x80x99 approach (Ried et al., 1992), uses only two xe2x80x98levelsxe2x80x99 (i.e., xe2x80x98onxe2x80x99 or xe2x80x98offxe2x80x99) to generate the colors. In this case, C=2Dxe2x88x921. For five different dyes, this scheme is capable of generating probes of 31 different colors, or enough spectral diversity to assign a unique color to each of the 24 human chromosomes for automated karyotyping. However, since the number of microbial species is very large, a five-channel system is insufficient to cover the complexity. Nevertheless, there are only a few published examples in which even two dyes have been used simultaneously for bacterial identification (Amann et al., 1990; Wallner et al., 1993; Gunderson and Goss, 1997), and at the present time no one has yet taken full advantage of FISH/SKY technology for three or more fluorophores for bacterial or other taxonomic identification. This is why we developed an eight-channel MTID imaging system with accompanying software for automated spectral deconvolution, sorting, and classification, as well as software and protocols for highly multiplexed probe design, labeling, and hybridization.